Method of determining the position of a landmark in the environment map of a self-propelled unit, the distance of the landmark from the unit being determined dynamically by the latter

ABSTRACT

The method solves the problem of assigning surveyed landmarks to landmarks in an environment map by each measured value being compared with a predicted measured value which has previously been calculated on the basis of an earlier measurement. A dynamic limit is prescribed for this system error. The assignment of a measured value to a landmark and hence the determination of the position of this landmark in the environment is ensured by the system error being positive and falling below a specific value. In the event that such an assignment is no longer possible, the limit is dynamically raised, specifically until the ratio of predicted landmarks to assigned landmarks falls below a specific value. In order to avoid endless loops in the method, which may be produced by no assignment being possible, the method is broken off after a specific number of loops. The method is suitable for robots in domestic, office and industrial environments, as well as for transport vehicles.

BACKGROUND OF THE INVENTION

In the prior art there are numerous possible uses for autonomouslyoperating mobile units. In this connection, one thinks of remote sensingprobes, of mobile units which operate in danger areas, of self-propelledindustrial vacuum cleaners, of transport vehicles in the productionindustry and, not least, of self-propelled robots. However, in order tobe able to fulfil a practical task in an a priori unknown environment,an autonomous mobile robot must be able both to construct a reliable mapof its working environment step by step and to locate itself at anygiven time using this map. Because of the very complex and unstructuredenvironments in which such self-propelled units may possibly manoeuvre,their areas of use often remain restricted to office and domesticenvironments. Since in general an a priori map is not available, such aself-propelled unit must be equipped with sensors which allow the unitto interact in a flexible manner with its environment. Some such sensorsare laser distance scanners, video cameras and ultrasonic sensors, forexample.

A particular problem of these mobile units is that the formation of theenvironment map and the locating of the mobile unit depend on eachother. In the process, various errors occur. On the one hand, such amobile unit measures the distance covered from a starting position, onthe other hand it uses distance sensors to measure the distance toobstacles which occur and enters these as landmarks in the environmentmap. Since these errors accumulate and add up over relatively longdistances, practical manoeuvrability of the mobile unit is no longergiven above a specific limit.

Previous solution formulations for this problem are based on the factthat characteristic landmarks in the environment are detected and theirpositions relative to the mobile unit are measured. From these relativepositions of the landmarks and the absolute position of the unit, theabsolute locations of the landmarks are determined. All the sensormeasurements are generally affected by uncertainties. For this reason,methods are used with which the best possible estimation for theposition of the mobile unit and the positions of the landmarks are foundat the same time. Previously there has been a wide variety offormulations for solving this problem.

In a method by Leonard and Durrant-Whyte Directed Sonar Sensing forMobile robot Navigation (pp. 51-65; pp. 97-111; pp. 129-138); KluwerAcademic Publishers, Boston, London, Dordrecht, 1992, the uncertaintyrelations between the landmarks and the mobile unit are not taken intoaccount. Thus, on the one hand, the computing time is drasticallyreduced but, on the other hand, the errors between the predicted andmeasured sensor values cannot be assigned to the respective errorsources. In order to ensure stability of the method, it is necessary topresuppose that the mobile unit can detect landmarks very accuratelywhen stationary. Very accurate sensors are thus required, which in turn,because of their high costs, also limit the practical use of this methodformation.

General statements about map building and about the motion of mobileunits in cartographic environments can be taken from Y. Bar-Shalom andT. E. Fortmann, Tracking and Data Association (pp. 52-61; pp. 100-109).Academic Press, 1988 Directed Sonar Sensing for Mobile robot Navigation(pp. 51-65; pp. 97-111; pp. 129-138); Kluwer Academic Publishers,Boston, London, Dordrecht, 1992.

In order that a mobile robot can determine its position within itsenvironment, the current sensor measurements must be correlated with aninternal map, that is to say it must be decided which measurement agreeswith which landmark in the map. From the assignment, the current robotposition can then be estimated. If the solutions which are used resultin few erroneous measurements, it is possible to find an assignment foreach measurement. If, however, erroneous measurement often occur, it isonly possible to-assign those measurements which, with high probability,agree with a landmark. For this purpose, a so-called validation gatemethod Y. Bar-Shalom and T. E. Fortmann, Tracking and Data Association(pp. 52-61; pp. 100-109), Academic Press, 1988; and Directed SonarSensing for Mobile robot Navigation (pp. 51-65; pp. 97-111; pp.129-138); Kluwer Academic Publishers, Boston, London, Dordrecht, 1992 isused. This method calculates, on the basis of the difference between themeasurement and the predicted measurement of the associated landmark andits respective uncertainties, the probability that the assignment iscorrect. For example, only those assignments whose statistical distancelies below a specific limit are accepted. If this limit is small, onlyvery statistically closely adjacent, and thus extremely certain,assignments are allowed. If the differences between the measurements andthe predicted measurements of the associated landmarks remain small overtime, this functions very well. In a real environment, however, thisrapidly leads to problems if landmarks are hidden by unknown objects. Aslong as the landmarks are hidden, no assignments are made in a correctmanner. If, thereafter, the landmarks become visible once more, it maybe the case that the estimated robot position has in the meantime becomeso erroneous, because of the slip of the drive wheels, that assignmentcan no longer be carried out because of the strict criterion, and theself-propelled mobile unit finally becomes lost in its environment. Onthe other hand, if this limit is selected to be too large, improbableassignments will also be allowed. The risk that false assignments willthen be made is very large, with the consequence that disastrous errorswhich cannot be eliminated again later can be made in determining theposition of the unit. In the solutions cited, on the basis of empiricalexperience, the parameter is selected such that an acceptable assignmentis carried out in most cases.

SUMMARY OF THE INVENTION

The invention is based on the object of specifying a method using whichan improved assignment of measurement results to the associatedlandmarks in the environment map of an autonomous mobile unit ispossible, and hence the determination of the position of the unit in theenvironment is improved.

In general terms the present invention is a method of determining theposition of a landmark in the environment map of a self-propelled unit,the distance of the landmark from the unit being determined dynamicallyby the latter. At least by the unit, a position change is determined byat least one first measuring means arranged on the unit. By the unit, atleast one distance of a first obstacle in the environment relative tothe unit is determined by at least one second measuring means arrangedon the unit, and is entered in the environment map as first landmark. Atleast at a starting position, the position of the unit and the locationof the first landmark in the environment map correspond to the realrelationships in the environment. After a movement away of the unit, atleast one first own position of the unit relative to the startingposition is determined with a position uncertainty. From the first ownposition, at least one first distance to the first obstacle isdetermined with a position uncertainty and a first distance to asuspected second obstacle, a determining landmark, is measured. From asecond own position, at least one second distance to the suspectedsecond obstacle is measured. For the second own position, using thefirst distance and the position change between the first own positionand the second own position, a predicted second distance to the firstand to the second obstacle is calculated. As the first and second systemerror, at least the difference between the measured second distance tothe suspected second obstacle and the calculated predicted seconddistance to the first and to the suspected second obstacle isdetermined. For determining the position of the first landmark, thelatter is allocated the position of the suspected second obstacle in theenvironment map if the magnitude of the second system error falls belowa fixed unit and is positive.

From at least two chronologically successive system errors, aprobability is calculated for the size of the system error. Only suchlandmarks as are located in front of the unit in the direction of travelare determined.

In the event that the position of a landmark can no longer bedetermined, since the system error is becoming too large, the limit israised.

The raising of the limit can be carried out only to the extent to whichthe ratio of the number of determined landmarks to the number ofpredicted landmarks does not exceed a fixed value, the reliabilitylimit.

The method carried out repeatedly and for the case in which no locationof a landmark can be determined, the number of repetitions isrestricted.

An odometer is used as first measuring means.

A distance meter is used as second measuring means.

The measurement inaccuracy of at least one measuring means is added tothe system error.

The measurement inaccuracy of at least one measuring means ispartitioned and one part is added to the position uncertainty andanother part to the location uncertainty.

The system error at time k is determined to be

    v(k+1)=z (k+1)-z (k+1|k)

and uncertainty of the system error results therefrom as ##EQU1## fromwhich the statistical distance between the measurement and the predictedmeasurement is derived as

    d(k)=v(k+1)S.sup.-1 (k)v.sup.T (k+1)

and

    d≦γ.sup.2

is prescribed as the fixed value which is a limit with γ_(max) =γ_(min)+n_(max) as prescribed range, where n is the maximum number of possibleiterations, with a measurement error R(k+1) where

    ______________________________________    v(k + 1)    is the system error at time k + 1    z(k + 1|k)                is the calculated predicted landmark                distance at time k + 1 with                z(k + 1|k) = h(x(k + 1|k), p.sub.t (k +                1|k))    x(k + 1|k)                is the predicted own position of the unit                at time k + 1    p.sub.t (k + 1|k)                is the predicted landmark position of the                unit at time k + 1    ∇h.sub.A(k+1|k)                is the jacobean of the function h, modulus                p.sub.t (k + 1|k)    ∇h.sub.x(k+1|k)                is the jacobean of the function                h, modulus x(k + 1|k)    P(k + 1|k)                is the covariance matrix of                x(k + 1|k) at time k + 1    Δ.sub.t (k + 1|k)                is the covariance matrix of                p.sub.t (k + 1|k) at time k + 1    Indices       indicate predicted value.    ______________________________________

In one embodiment R(k+1)=0 applies.

In another embodiment the following limits apply: γ_(max) =4, γ_(min) =2n_(max) =3 and, as reliability limit, 1.5.

A particular advantage of the method according to the invention consistsin the fact that measurements which cannot be assigned unambiguously toa landmark can be distinguished in relation to the associated landmarkvia the system error and a prescribed positive limit. The positive limitin this case means that the predicted value should be smaller than thecurrent measured value. Thus, it is advantageously ensured by the methodaccording to the invention that, in dynamically changeable environments,for example when an obstacle gets between a landmark and theself-propelled mobile unit between two measurement intervals, thislandmark is not allocated a false value.

In an advantageous manner, by means of the method according to theinvention, from the quantity of landmarks which are present in theenvironment map only those are evaluated which are located within anarea in front of the unit in the direction of travel.

In a particularly advantageous manner, as a result of the methodaccording to the invention, no fixed limit is placed on the systemerror, but rather, this unit is made dynamic. Thus, the position oflandmarks which can be determined only inaccurately can still beassigned to an object in the environment map.

In a particularly advantageous manner, the inaccuracy which is producedby raising the limit in the inventive method for the inherent systemerror is restricted by continuously checking how the number ofdetermined landmarks behaves in relation to the number of predictedlandmarks. A rise in the number of determined landmarks could signify,for example, double assignments. Such occurrences are advantageouslyavoided thereby. In order to be able to end the method when nodetermination of the position of a landmark is possible, a limit isadvantageously prescribed for its number of cycles.

The use of an odometer as distance meter for the method according to theinvention is particularly favorable in the case of wheel-drivenvehicles.

Depending on the field of use of the mobile unit, a cost-effectivedistance meter, with which landmarks can be detected, can favorably beused for the method according to the invention.

It is advantageous to take into account the measurement uncertainty ofthe sensor for the distance measurement, since in this way the currentposition or its uncertainty of a landmark can be detected moreaccurately.

In a particularly advantageous manner, the number of possible iterationsn is included for the upper limit of γ, in order to achieve a practicalaccuracy in determining the position of the landmarks. It isadvantageous for office environments to select the values 2 for γ_(min),4 for γ_(max), 3 for n_(max) and 1.49 as the reliability ratio.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the present invention which are believed to be novel,are set forth with particularity in the appended claims. The invention,together with further objects and advantages, may best be understood byreference to the following description taken in conjunction with theaccompanying drawings, in the several Figures of which like referencenumerals identify like elements, and in which:

FIG. 1 shows a self-propelled unit in an environment.

FIG. 2 shows the partitioning of the system error.

BRIEF DESCRIPTION OF THE PREFERRED EMBODIMENTS

The fundamentals of the method according to the invention are shown inFIG. 1 in order to explain the terms. A mobile unit E is shown invarious positions which are characteristic for the method according tothe invention. Furthermore, a landmark L can be seen, the location ofwhich is determined.

At the beginning of its movement, the mobile unit E is located here, forexample, at a position P1K, which is subject to an uncertainty U1K. Thisposition uncertainty is represented by an ellipse. Following a movementoperation, the unit is located at a position P2P. This position P2P is apredicted position, that is to say on the basis of the measurementsusing an odometer, for example, the mobile unit should now be located atthe position P2P in relation to the starting position P1K. However, thisposition P2P does not correspond to the real residence location of theunit in the environment, since the distance measurement is subject toerror. The position P2P is stored as current position, for example in anenvironment map in an orientation computer of the mobile unit, and isentered as the current position in the environment map. On the basis ofa distance measurement, carried out from the position P1K, to thelandmark in LR, a distance MP from the position P2P to the assumedlocation LP of the landmark is now predicted. The location LP of thelandmark L in this case has an uncertainty ULP which is represented byan ellipse.

However, the position P2P is not the real position which is assumed bythe unit. In actual fact, it is located at the position P2R. By means ofa measurement using a distance meter, for example a laser radar or anultrasonic sensor, the real distance of the unit MR from the location ofthe landmark LR is now determined. It should be noted here that neitherthe position P2R nor the location LR of the landmark has an uncertainty,since these are the real positions. In the event that use is made ofdistance meters which have an error, it would be necessary to take thiserror into account here. However, its processing is not necessary forcarrying out the method according to the invention.

As is described in more detail in the cited literature, the position ofa mobile unit is, for example, described in the form of coordinates andin the form of an orientation which specifies an angle of rotation ofthe unit in the coordinate system. From this, it can be seen that adistance measurement is also a function of the rotational orientation ofthe mobile unit in a coordinate system. For example, these rotationalorientations and the distance are to be taken into account in correctingthe position and the landmark of the mobile unit.

Since the variables which are a precondition for the method according tothe invention are now available, this method can be carried out.

This means that the real distance MR is now compared with the predicteddistance and, for example, the difference v is formed. This differencehas an uncertainty as a function of the location uncertainty ULP and ofthe position uncertainty U2P. This uncertainty may, for example, beappropriately partitioned and the location of the landmark and theposition of the mobile unit in the environment map can be corrected withthe respective component. One then obtains for the landmark, forexample, the position LK with an uncertainty ULK, in which case careshould be taken that the uncertainty ULK is smaller than the uncertaintyULP. For the mobile unit, after carrying the method according to theinvention, one obtains the position P2K with an uncertainty U2K, itbeing true once more that this uncertainty U2K is smaller than theuncertainty U2P.

When using these new values for a further step of the method accordingto the invention in the starting map, it is possible to settle on asmaller uncertainty and hence determine new positions of the unit andnew locations of landmarks with a greater accuracy.

FIG. 2 illustrates the calculation of the system error in accordancewith the method according to the invention. The real measurement MR andthe predicted distance measurement MP are shown. The difference betweenthese two distances yields the system error v, for example. This systemerror is now partitioned, for example into a location correction LKO anda position correction PK. The partitioning is carried out for example inaccordance with the size of the respective uncertainty with which thedetermination of position or the determination of location is affected.Using the location correction LKO, the location of a landmark may becorrected and, using the position correction PK, the position of themobile unit in the environmental map may be reestablished. Here, careshould be taken that, during the correction, both the position of themobile unit in a coordinate system at a specific time and the directionof rotation of the mobile unit are to be taken into account.

The correction of the respective positions or location may then becarried out by applying the corresponding angular functions to thecoordinate values. During the assignment of a measured value from alandmark to a landmark present in the environmental map, this error isuseful as a limit for the reliability of an assignment. In this case,the error should, for example initially, not exceed a fixed value.

The method according to the invention is based on a dynamically variedvalidation gate variable. An attempt is firstly made, using a strictcriterion, to assign the measurements to the landmarks and hence todetermine their position in the environmental map and the position ofthe mobile unit in relation thereto. If sufficient assignments arefound, for example a specific percentage of the predicted assignmentsare actually accepted, then only those assignments which fulfill thisstrict criterion are selected. If this is not possible, for one of thereasons outlined at the beginning, the validation gate is enlargedsomewhat, in order also to allow somewhat less certain assignments. Thisoperation is continued until, for example, the allowed assignmentsexceed a specific percentage of the predicted assignments, or else amaximum number of steps has been carried out. The latter is alsonecessary, since in specific situations, if for example a region hasbeen completely altered, it is never possible to fulfil the percentagecriterion. For example, for the relationship between the initial sizeγ_(min), the maximum size γ_(max) and the maximum number of iterations,the following formula is selected:

    γ.sub.max =γ.sub.min +n.sub.max

The procedure may for example be as follows:

1) Calculate, using the formulae 1 to 3, for each possible assignmentthe corresponding statistical distance d(k). The set of theseassignments is identified by M(k). In this case, the formulae are:

    v(k+1)=z(k+1)-z(k+1|k)                            (1)

    d(k)=v(k+1)S.sup.-1 (k)v.sup.T (k+1)                       (2) ##EQU2## 2) Select, from M(k), the assignments whose distance is smaller than γ.sub.max.sup.2. The new set is identified by M.sub.max. 3) Select, from M.sub.max, the assignments with v>0, since the landmarks are assumed not to be hidden. The new set is identified by M.sub.vis. This step is necessary since otherwise erroneous assignments are made in the case of a relatively large validation gate.

4) The number of predicted assignments N_(pred) results from thecardinal number of M_(vis). The number of accepted assignments isidentified by N_(acc).

5) Set γ=γ_(min), n=0 and N_(acc) =0.

6) From the set M_(max), look for the assignments which are accepted onthe basis of formula (4).

    d≦γ.sup.2                                     (4)

7) If it is true that N_(acc) /N_(pred) ≦T and n<n_(max), increment nand hence γy. T is the percentage threshold.

8 Repeat steps 6 and 7 until the conditions for breaking off aresatisfied.

By way of example, in quite typical office environments, values ofγ_(min) =2, γ_(min) =4, n_(max) =3 and T=0.6 result in an increase of upto 200% in the accuracy of the determination of the position oflandmarks by contrast with conventional methods.

The invention is not limited to the particular details of the methoddepicted and other modifications and applications are contemplated.Certain other changes may be made in the above described method withoutdeparting from the true spirit and scope of the invention hereininvolved. It is intended, therefore, that the subject matter in theabove depiction shall be interpreted as illustrative and not in alimiting sense.

What is claimed is:
 1. A method of determining a position of a landmarkin an environment map of a self-propelled unit, a distance of thelandmark from the unit being determined dynamically by the unit,comprising the steps of:a) determining, at least by the unit, a positionchange using at least one first measuring device arranged on the unit;b) determining, by the unit, at least one distance of a first obstaclein the environment relative to the unit using at least one secondmeasuring device arranged on the unit, and entering the first obstaclein the environment map as a first landmark; c) corresponding, at leastat a starting position of the unit, the position of the unit and thelocation of the first landmark in the environment map to realrelationships in the environment; d) determining, after a movement ofthe unit away from the starting position, at least one first position ofthe unit relative to the starting position with a position uncertainty;e) determining, from the first position of the unit, at least one firstdistance to the first obstacle with a location uncertainty and measuringa first distance to a suspected second obstacle, a determining landmark;f) measuring, from a second position of the unit, at least one seconddistance to the suspected second obstacle; g) calculating, for thesecond position of the unit, using the first distance to the firstobstacle and a position change between the first position of the unitand the second position of the unit, a predicted second distance to thefirst obstacle and to the suspected second obstacle; h) determining, asa system error, at least a difference between the measured seconddistance to the suspected second obstacle and the calculated predictedsecond distance to the suspected second obstacle; and i) allocating, fordetermining the position of the first landmark, the position of thefirst landmark to the position of the suspected second obstacle in theenvironment map if a magnitude of the system error falls below a fixedvalue and is positive.
 2. The method as claimed in claim 1, wherein,from at least two chronologically successive system errors, aprobability is calculated for a magnitude of the system error, andwherein only such landmarks as are located in front of the unit in adirection of travel are determined.
 3. The method as claimed in claim 1,wherein the fixed value is a limit and wherein when the position of alandmark can no longer be determined, since the system error is becomingtoo large, the limit is raised.
 4. The method as claimed in claim 3,wherein raising of the limit can be carried out only to an extent that aratio of a number of determined landmarks to a number of predictedlandmarks does not exceed a further fixed value, which is a reliabilitylimit.
 5. The method as claimed in claim 1, wherein the method iscarried out repeatedly and wherein when no location of a landmark isdeterminable, the number of repetitions is restricted.
 6. The method asclaimed in claim 1, wherein the first measuring device is an odometer.7. The method as claimed in claim 1, wherein the second measuring deviceis a distance meter.
 8. The method as claimed in claim 1, whereinmeasurement inaccuracy of at least one measuring device of the first andsecond measuring devices is added to a system error.
 9. The method asclaimed in claim 1, wherein measurement inaccuracy of at least onemeasuring device of the first and second measuring devices ispartitioned and wherein one part of the measurement inaccuracy is addedto the position uncertainty and another part of the measurementinaccuracy is added to the location uncertainty.
 10. The method asclaimed in claim 1, whereinsystem error at time k is determined to be

    v(k+1)=z(k+1)-z(k+1|k)

and uncertainty of the system error results therefrom as ##EQU3## fromwhich the statistical distance between the measurement and the predictedmeasurement is derived as

    d(k)=v(k+1)S.sup.-1 (k)v.sup.T (k+1)

and

    d≦γ.sup.2

is prescribed as the fixed value which is a limit with γ_(max) =γ_(min)+n_(max) as prescribed range, where n is the maximum number of possibleiterations, with a measurement error R(k+1) where

    ______________________________________    v(k + 1)     is the system error at time k + 1    z(k + 1|k)                 is the calculated predicted                 landmark distance at time k + 1 with                 z(k + 1|k) = h(x(k + 1|k), p.sub.t (k +                 1|k))    x(k + 1|k)                 is the predicted own position                 of the unit at time k + 1    p.sub.t (k + 1|k)                 is the predicted landmark                 position of the unit at time k + 1    ∇h.sub.A(k+1|k)                 is the jacobean of the function                 h, modulus p.sub.t (k + 1|k)    ∇h.sub.x(k+1|k)                 is the jacobean of the function                 h, modulus x(k + 1|k)    P(k + 1|k)                 is the covariance matrix of                 x(k + 1|k) at time k + 1    Λ.sub.t (k + 1|k)                 is the covariance matrix of                 p.sub.t (k + 1|k) at time k + 1    Indices      indicate predicted value.    ______________________________________


11. The method as claimed in claim 10, wherein R(k+1)=0.
 12. The methodas claimed in claim 10, wherein γ_(max) =4, _(m) γ_(n) =2_(max) n=3 andthe reliability limit is 1.5.